检索结果-内蒙古大学图书馆

Cloud Computing, Big Data Applications and Software Engineering (CBASE), International conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EF-BV: a unified theory of error feedback and variance reduction mechanisms for biased and unbiased compression in distributed optimization 22

EF-BV: a unified theory of error feedback and variance reduc...

引用

Proceedings of the 36th International conference on Neural Information processing Systems

作者： Laurent Condat Kai Yi Peter Richtárik KAUST King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

ISBN: (纸本)9781713871088

In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce the number of bits sent within each communication round of iterative methods. There are two classes of compression operators and separate algorithms making use of them. In the case of unbiased random compressors with bounded variance (e.g., rand-k), the DIANA algorithm of Mishchenko et al. (2019), which implements a variance reduction technique for handling the variance introduced by compression, is the current state of the art. In the case of biased and contractive compressors (e.g., top-k), the EF21 algorithm of Richtárik et al. (2021), which instead implements an error-feedback mechanism, is the current state of the art. These two classes of compression schemes and algorithms are distinct, with different analyses and proof techniques. In this paper, we unify them into a single framework and propose a new algorithm, recovering DIANA and EF21 as particular cases. Our general approach works with a new, larger class of compressors, which has two parameters, the bias and the variance, and includes unbiased and biased compressors as particular cases. This allows us to inherit the best of the two worlds: like EF21 and unlike DIANA, biased compressors, like top-k, whose good performance in practice is recognized, can be used. And like DIANA and unlike EF21, independent randomness at the compressors allows to mitigate the effects of compression, with the convergence rate improving when the number of parallel workers is large. This is the first time that an algorithm with all these features is proposed. We prove its linear convergence under certain conditions. Our approach takes a step towards better understanding of two so-far distinct worlds of communication-efficient distributed learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parameter Servers Placement for distributed Deep Learning in Edge computing

Parameter Servers Placement for Distributed Deep Learning in...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Jiaquan Yan Yalan Wu Jigang Wu Long Chen School of Computer Science and Technology Guangdong University of Technology Guangzhou China

The architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. Therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. Then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Training Deep learning NP-hard problem Computational modeling Heuristic algorithms Simulation Optimized production technology

来源：评论

学校读者我要写书评

暂无评论

Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

引用

Symposium of image, Signal processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem parallel processing Scheduling Proposals

来源：评论

学校读者我要写书评

暂无评论

Zero-touch security automation mechanisms for edge NFV: the π-Edge approach

Zero-touch security automation mechanisms for edge NFV: the ...

引用

International conference on Network and Service Management

作者： Alexandros Valantasis Nikos Psaromanolakis Vasileios Theodorou Intracom Telecom Athens Greece

ISBN: (数字)9783903176515

ISBN: (纸本)9781665489928

The shift towards distributed computing architectures that push data storage and processing to the edge of the network, is resulting into a convergence of cloud-computing services and next generation mobile network technologies. In order to uniformly manage resources and services in the formed cloud/core to edge/devices continuum and to handle the diversity of multi-party underlying infrastructure technologies in a latency-aware, reliable and trustworthy fashion, management automation has become more crucial than ever. In this work, we present the security analytics mechanisms of the π-Edge platform–our edge management platform that embodies zero-touch automation features for interoperability, Quality of Service (QoS) assurance, resilience and trust. To this end, we introduce a declarative NFV MANO Information Model (IM) and methods for automatically enhancing Network Slices at the edge, with security services that i) continuously monitor user-plane traffic on the links between Virtual Network Functions (VNFs), ii) detect possible network vulnerabilities or malicious behaviour and iii) apply relevant actions to effectively observe and mitigate identified threats. The implementation of such mechanisms is evaluated through experimentation on a use case of DDoS attacking scenarios, showcasing the usability and the benefits of our proposed solution.

关键词： Cloud computing Automation image edge detection Quality of service Time measurement Security Resource management

来源：评论

学校读者我要写书评

暂无评论

Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data

Entropy and Autoencoder-Based Outlier Detection in Mixed-Typ...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhongyang Wang Yijie Wang Zhenyu Huang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Telecommunication traffic Machine learning Detectors Network security Feature extraction Entropy Data models

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Deep Learning Framework for Brain Tumor Classification through Histopathological image

Expression of Concern for: Deep Learning Framework for Brain...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： Sandhiya B UshaKiruthika S Kanaga Suba Raja S Department of CSE Shiv Nadar University Chennai Easwari Engineering College Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474146

Further most oncologist varies in their conclusions around the determination utilizing histopathological-pictures, so the manual diagnosis of brain tumour is troublesome, and it too needs elevated involvement. Building a show of brain tumour classification is a fundamental assignment. CAD empowers productive and exact conclusion of this sort of imaging. In addition, it makes a difference early determination of brain tumours. Profound learning has started to advance within the final decades, so convolutional neural systems/networks are utilized to classify brain tumours. We presented the programmed brain tumour classification strategies to assist specialists in examination and treatment, which can naturally classify the histopathological pictures as generous or dangerous. This study’s hypothetical premise is creating a profound learning tumour classification framework. It employs a profound learning approach that is the proposed CNN model comprises of three stages. The primary stage is pre-processing. The moment stage is the highlight extraction organize that takes input as augmented preprocessed pictures. This stage has two divisions. The primary stage comprises of three parallel CNN branches with different filter sizes to extricate distinctive include levels. We blended the three parallel branches within the moment stage, which produced a highlight combination way that diminished the time complexity and upgraded the outlined methods, performance. The third stage classifies the melded highlights as generous or dangerous. The Cancer-Genome-Atlas Glioblastoma Multiform-DB (TCGA-GBM) is utilized &implemented and contains 19,507 files of brain tumour tissue for 237 patients. The data was parted into three sections: 60%, 20%, 20% for training, validation, and testing. A few tests were performed to attain the

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards distributed Bitruss Decomposition on Bipartite Graphs

引用

PROCEEDINGS OF THE VLDB ENDOWMENT 2022年第9期15卷 1889-1901页

作者： Wang, Yue Xu, Ruiqi Jian, Xun Zhou, Alexander Chen, Lei Shenzhen Inst Comp Sci Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Mining cohesive subgraphs on bipartite graphs is an important task. The k-bitruss is one of many popular cohesive subgraph models, which is the maximal subgraph where each edge is contained in at least k butterflies. The bitruss decomposition problem is to find all k-bitrusses for k >= 0. Dealing with large graphs is often beyond the capability of a single machine due to its limited memory and computational power, leading to a need for efficiently processing large graphs in a distributed environment. However, all current solutions are for a single machine and a centralized environment, where processors can access the graph or auxiliary indexes randomly and globally. It is difficult to directly deploy such algorithms on a shared-nothing model. In this paper, we propose distributed algorithms for bitruss decomposition. We first propose SC-HBD as the baseline, which uses H-function to define bitruss numbers and computes them iteratively to a fix point in parallel. We then introduce a subgraph-centric peeling method SC-PBD, which peels edges in batches over different butterfly complete subgraphs. We then introduce local indexes on each fragment, study the butterfly-aware edge partition problem including its hardness, and propose an effective partitioner. Finally we present the bitruss butterfly-complete subgraph concept, and divide and conquer DC-BD method with optimization strategies. Extensive experiments show the proposed methods solve graphs with 30 trillion butterflies in 2.5 hours, while existing parallel methods under shared-memory model fail to scale to such large graphs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GPU parallel implementation of improved noise adaptive principal component algorithm for feature extraction of hyperspectral images

GPU parallel implementation of improved noise adaptive princ...

引用

Applied Optics and Photonics China (AOPC) conference - MEMS, THz MEMS, and Metamaterials and AI in Optics and Photonics

作者： Li, ChunChao Wu, Luting Peng, Yuanxi Liu, Yu Xu, Yunpeng Natl Univ Def Technol State Key Lab High Performance Comp Coll Comp Chang Sha 410073 Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Chang Sha 410073 Peoples R China

ISBN: (数字)9781510639522

ISBN: (纸本)9781510639522

The classification of Hyperspectral images (HSIs) has been the focus of many recent research efforts, where feature extraction plays an important role. Discriminative feature extraction methods aim to reduce the data dimension of HSIs, retain effective image information to the greatest extent, and suppress noises at the same time. Besides, according to the characteristics of pixel-by-pixel-multi-band of HSIs and data redundancy between bands, the processing of HSIs in the classifier will bring huge computational overhead. In this paper, we present a parallel implementation of the improved noise adaptive principal component algorithm (INAPC) for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). Aiming at maximizing the signal-to-noise ratio (SNR) instead of the variance, we firstly deploy two SVDs and more comprehensive noise estimation in the INAPC transform and constructed a complete feature extraction process. Then we deploy a complete CPU-GPU collaborative computing solution, and use several GPU programming optimization methods to achieve the maximum acceleration effect. Through the experiments on three real hyperspectral datasets, Experimental results show that the proposed INAPC has stable superiority and provides a significant speedup compared to the CPU implementation.

关键词： Graphics processing units hyperspectral imaging feature extraction parallel improved noise adaptive principal component algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient image Watercolorization Based on Smart Phones

Efficient Image Watercolorization Based on Smart Phones

引用

2020 International conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020

作者： Zhang, Huijuan Xiao, Xuezhong School of Computer Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728165110

image watercolorization is a hot research topic in the field of non-photorealistic rendering. There are many methods proposed to simulate the various features of watercolor painting including edge darkening, pigment dispersion, turbulence, and paper texture. However, almost all of them are implemented for desktop systems and cannot be directly transplanted to mobile devices. From the view of resultant effects and computing efficiency, an image watercolorization framework is proposed for smart phones, which employs several existing image processing algorithms to simulate the visual features of watercolor painting. Finally, the method is optimized in parallel to improve the efficiency. The experiments show the presented method has good performance. © 2020 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：